Brain Rank |
Model |
Brain-Score |
V1: Your data here! |
V2: Your data here! |
Behavior: dicarlo.Raja...2018 |
Classification: Imagenet2012 |
||
---|---|---|---|---|---|---|---|---|
Ceiling | .729 | .892 | .817 | .479 | 100.0 | |||
1 | densenet-169 Huang et al., 2016 |
.549 | .663 | .606 | .378 | 75.9 | ||
2 | cornet_s Kubilius et al., 2018 |
.544 | .650 | .600 | .382 | 74.7 | ||
3 | resnet-101_v2 He et al., 2015 |
.542 | .653 | .585 | .389 | 77.4 | ||
4 | densenet-201 Huang et al., 2016 |
.541 | .655 | .601 | .368 | 77.2 | ||
5 | densenet-121 Huang et al., 2016 |
.541 | .657 | .597 | .369 | 74.5 | ||
6 | resnet-152_v2 He et al., 2015 |
.541 | .658 | .589 | .377 | 77.8 | ||
7 | resnet-50_v2 He et al., 2015 |
.540 | .653 | .589 | .377 | 75.6 | ||
8 | xception Chollet et al., 2016 |
.533 | .671 | .565 | .361 | 79.0 | ||
9 | inception_v2 Szegedy et al., 2015 |
.532 | .646 | .593 | .357 | 73.9 | ||
10 | inception_v1 Szegedy et al., 2014 |
.532 | .649 | .583 | .362 | 69.8 | ||
11 | resnet-18 He et al., 2015 |
.531 | .645 | .583 | .364 | 69.8 | ||
12 | nasnet_mobile Zoph et al., 2017 |
.530 | .650 | .598 | .342 | 74.0 | ||
13 | pnasnet_large Liu et al., 2017 |
.528 | .644 | .590 | .351 | 82.9 | ||
14 | inception_resnet_v2 Szegedy et al., 2016 |
.528 | .639 | .593 | .352 | 80.4 | ||
15 | nasnet_large Zoph et al., 2017 |
.527 | .650 | .591 | .339 | 82.7 | ||
16 | mobilenet_v2_0.75_224 Howard et al., 2017 |
.527 | .613 | .590 | .377 | 69.8 | ||
17 | vgg-19 Simonyan et al., 2014 |
.525 | .672 | .566 | .338 | 71.1 | ||
18 | mobilenet_v2_1.4_224 Howard et al., 2017 |
.525 | .626 | .600 | .348 | 75.0 | ||
19 | inception_v4 Szegedy et al., 2016 |
.524 | .628 | .575 | .371 | 80.2 | ||
20 | mobilenet_v1_1.0_224 Howard et al., 2017 |
.524 | .623 | .601 | .347 | 70.9 | ||
21 | mobilenet_v2_1.3_224 Howard et al., 2017 |
.523 | .619 | .595 | .356 | 74.4 | ||
22 | inception_v3 Szegedy et al., 2015 |
.523 | .646 | .587 | .335 | 78.0 | ||
23 | mobilenet_v2_0.75_192 Howard et al., 2017 |
.522 | .613 | .594 | .359 | 68.7 | ||
24 | resnet-34 He et al., 2015 |
.522 | .629 | .559 | .378 | 73.3 | ||
25 | mobilenet_v2_1.0_192 Howard et al., 2017 |
.522 | .601 | .595 | .369 | 70.7 | ||
26 | vgg-16 Simonyan et al., 2014 |
.521 | .669 | .572 | .321 | 71.5 | ||
27 | mobilenet_v1_0.75_224 Howard et al., 2017 |
.519 | .618 | .592 | .346 | 68.4 | ||
28 | mobilenet_v1_0.75_192 Howard et al., 2017 |
.517 | .620 | .592 | .340 | 67.2 | ||
29 | mobilenet_v2_1.0_224 Howard et al., 2017 |
.517 | .612 | .591 | .348 | 71.8 | ||
30 | mobilenet_v1_1.0_160 Howard et al., 2017 |
.517 | .632 | .592 | .327 | 68.0 | ||
31 | mobilenet_v1_1.0_192 Howard et al., 2017 |
.516 | .629 | .594 | .325 | 70.0 | ||
32 | mobilenet_v2_0.5_224 Howard et al., 2017 |
.514 | .622 | .588 | .332 | 65.4 | ||
33 | mobilenet_v2_1.0_160 Howard et al., 2017 |
.513 | .602 | .599 | .337 | 68.8 | ||
34 | mobilenet_v2_0.5_192 Howard et al., 2017 |
.510 | .616 | .586 | .329 | 63.9 | ||
35 | mobilenet_v2_0.75_160 Howard et al., 2017 |
.509 | .605 | .594 | .328 | 66.4 | ||
36 | mobilenet_v2_0.35_224 Howard et al., 2017 |
.507 | .627 | .580 | .314 | 60.3 | ||
37 | mobilenet_v2_0.5_160 Howard et al., 2017 |
.506 | .625 | .582 | .310 | 61.0 | ||
38 | mobilenet_v1_0.5_224 Howard et al., 2017 |
.505 | .604 | .585 | .326 | 63.3 | ||
39 | mobilenet_v1_0.5_192 Howard et al., 2017 |
.503 | .614 | .578 | .318 | 61.7 | ||
40 | mobilenet_v1_1.0_128 Howard et al., 2017 |
.502 | .623 | .575 | .308 | 65.2 | ||
41 | mobilenet_v1_0.75_160 Howard et al., 2017 |
.502 | .623 | .581 | .301 | 65.3 | ||
42 | mobilenet_v2_1.0_128 Howard et al., 2017 |
.501 | .601 | .591 | .310 | 65.3 | ||
43 | mobilenet_v2_0.35_160 Howard et al., 2017 |
.500 | .619 | .577 | .303 | 55.7 | ||
44 | mobilenet_v2_0.35_192 Howard et al., 2017 |
.499 | .629 | .579 | .290 | 58.2 | ||
45 | mobilenet_v1_0.75_128 Howard et al., 2017 |
.498 | .636 | .573 | .284 | 62.1 | ||
46 | mobilenet_v2_1.0_96 Howard et al., 2017 |
.496 | .613 | .578 | .297 | 60.3 | ||
47 | mobilenet_v2_0.75_128 Howard et al., 2017 |
.496 | .608 | .578 | .302 | 63.2 | ||
48 | mobilenet_v1_0.5_160 Howard et al., 2017 |
.495 | .621 | .576 | .289 | 59.1 | ||
49 | mobilenet_v2_0.75_96 Howard et al., 2017 |
.494 | .620 | .575 | .286 | 58.8 | ||
50 | mobilenet_v2_0.5_128 Howard et al., 2017 |
.489 | .607 | .574 | .287 | 57.7 | ||
51 | alexnet Krizhevsky et al., 2012 |
.488 | .631 | .589 | .245 | 57.7 | ||
52 | mobilenet_v1_0.5_128 Howard et al., 2017 |
.479 | .623 | .558 | .256 | 56.3 | ||
53 | mobilenet_v1_0.25_224 Howard et al., 2017 |
.478 | .619 | .574 | .240 | 49.8 | ||
54 | mobilenet_v2_0.5_96 Howard et al., 2017 |
.476 | .611 | .555 | .262 | 51.2 | ||
55 | mobilenet_v2_0.35_128 Howard et al., 2017 |
.472 | .613 | .550 | .253 | 50.8 | ||
56 | squeezenet1_1 Iandola et al., 2016 |
.469 | .652 | .553 | .201 | 57.5 | ||
57 | mobilenet_v2_0.35_96 Howard et al., 2017 |
.465 | .610 | .540 | .244 | 45.5 | ||
58 | mobilenet_v1_0.25_192 Howard et al., 2017 |
.464 | .600 | .551 | .242 | 47.7 | ||
59 | mobilenet_v1_0.25_160 Howard et al., 2017 |
.459 | .605 | .544 | .228 | 45.5 | ||
60 | mobilenet_v1_0.25_128 Howard et al., 2017 |
.455 | .619 | .527 | .220 | 41.5 | ||
61 | squeezenet1_0 Iandola et al., 2016 |
.454 | .641 | .542 | .180 | 57.5 |
About
The Brain-Score platform aims to yield strong computational models of the ventral stream. We enable researchers to quickly get a sense of how their model scores against standardized brain benchmarks on multiple dimensions and facilitate comparisons to other state-of-the-art models. At the same time, new brain data can quickly be tested against a wide range of models to determine how well existing models explain the data.
Brain-Score is organized by the DiCarlo lab @ MIT in collaboration with other labs worldwide. We are working towards making this into an easy-to-use platform where a model can easily be submitted to yield its scores on a range of brain benchmarks and new benchmarks can be incorporated to challenge the models.
This quantified approach lets us keep track of how close our models are to the brain on a range of experiments (data) using different evaluation techniques (metrics). For more details, please refer to the paper.
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Participate
Challenge the data: Submit a model
Please get in touch with us to have us score your model (we are working on automating this step).Challenge the models: Submit data
If you have neural or behavioral recordings that you would like models to compete on, please get in touch with us.Change the evaluation: Submit a metric
If you have an idea for a different way of comparing brain and machine, please get in touch with us.Citation
If you use Brain-Score in your work, please cite Brain-Score: Which Artificial Neural Network for Object Recognition is most Brain-Like?@article{SchrimpfKubilius2018BrainScore, title={Brain-Score: Which Artificial Neural Network for Object Recognition is most Brain-Like?}, author={Martin Schrimpf and Jonas Kubilius and Ha Hong and Najib J. Majaj and Rishi Rajalingham and Elias B. Issa and Kohitij Kar and Pouya Bashivan and Jonathan Prescott-Roy and Kailyn Schmidt and Daniel L. K. Yamins and James J. DiCarlo}, journal={bioRxiv preprint}, year={2018} }